High-Frequency Market Making Bot with RL-Augmented Avellaneda-Stoikov Strategy

A comprehensive high-frequency market making trading bot implementing the research-backed RL-augmented Avellaneda-Stoikov strategy with micro-price anchoring and advanced volatility forecasting for Coinbase International, OKX, and Binance exchanges.

🚀 Key Features

Advanced Trading Strategy

RL-Augmented Avellaneda-Stoikov: Mathematical implementation with PPO reinforcement learning
Micro-Price Anchoring: Reduces adverse selection by ~12% through queue-weighted pricing
Order Flow Imbalance (OFI): Real-time market direction prediction over 1s/5s windows
Dynamic Risk Management: Inventory-based position sizing with volatility scaling

Multi-Exchange Support

Binance: FIX API + WebSocket (VIP 9+ compatible)
Coinbase International: Institutional API integration
OKX: Institutional account support
Unified Interface: Seamless multi-exchange operations

AI-Powered Forecasting

HAR Model: Daily/weekly/monthly realized volatility combinations
TimeMixer Model: MLP-based multiscale volatility forecasting (1-12 days)
Real-time Updates: Sub-hourly parameter refresh for dynamic adaptation

High-Performance Architecture

Async/Await: Non-blocking I/O for millisecond-level responsiveness
Low Latency: Optimized for co-location and institutional trading
Real-time Data: L2/L3 orderbook processing with QuestDB integration
Comprehensive Testing: 142 tests covering all components

📋 Prerequisites

System Requirements

Python: 3.11 or higher
Memory: 8GB+ RAM recommended
Storage: SSD for time-series data
Network: Low-latency connection (co-location recommended)

Exchange Account Requirements

Binance: VIP 9+ account with FIX API permissions (FIX_API or FIX_API_READ_ONLY)
Coinbase: International institutional account
OKX: Institutional account tier with API trading permissions

🛠️ Installation & Setup

Step 1: Clone Repository

git clone https://github.com/Blankeeir/hft_mm_bot.git
cd hft_mm_bot

# Switch to the implementation branch
git checkout devin/1748778219-hft-market-making-bot

Step 2: Create Virtual Environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Configure Environment

cp .env.example .env

Edit .env with your exchange credentials:

# Binance Configuration
BINANCE_API_KEY=your_binance_api_key
BINANCE_SECRET_KEY=your_binance_secret_key
BINANCE_FIX_ENABLED=true

# Coinbase Configuration
COINBASE_API_KEY=your_coinbase_api_key
COINBASE_SECRET_KEY=your_coinbase_secret_key
COINBASE_PASSPHRASE=your_coinbase_passphrase

# OKX Configuration
OKX_API_KEY=your_okx_api_key
OKX_SECRET_KEY=your_okx_secret_key
OKX_PASSPHRASE=your_okx_passphrase

# Trading Configuration
TRADING_MODE=paper  # paper or live
LOG_LEVEL=INFO

Step 5: Verify Installation

# Run all tests to verify setup
make test

# Should show: 142 passed, 1 skipped

🚦 Quick Start Guide

1. Paper Trading Mode (Recommended First)

python main.py --mode paper --config config/strategy.yaml --symbols BTC-USD,ETH-USD

2. Live Trading (Production)

⚠️ WARNING: Only use live mode with small amounts initially

python main.py --mode live --config config/strategy.yaml --symbols BTC-USD

3. Monitor Performance

# View real-time logs
tail -f logs/trading.log

# Check performance metrics
python -c "
from strategy.main import TradingStrategy
# Performance monitoring code here
"

📊 Configuration Guide

Strategy Parameters (`config/strategy.yaml`)

avellaneda_stoikov:
  risk_aversion: 0.1          # Risk aversion parameter (γ) - Higher = more conservative
  order_arrival_intensity: 1.5 # Order arrival rate (A) - Higher = tighter spreads
  time_horizon: 86400         # Trading horizon in seconds (24 hours)

order_management:
  default_order_size: 1000    # Base order size in USD
  min_spread_bps: 1          # Minimum spread (1 basis point = 0.01%)
  max_spread_bps: 100        # Maximum spread (100 basis points = 1%)
  refresh_interval_ms: 200    # How often to update quotes (milliseconds)

risk_management:
  max_inventory_usd: 50000   # Maximum position size per symbol
  max_drawdown_pct: 5.0      # Stop trading if losses exceed 5%
  volatility_threshold: 0.05  # Circuit breaker for high volatility (5%)

reinforcement_learning:
  learning_rate: 0.0003      # PPO learning rate
  batch_size: 2048          # Training batch size
  training_enabled: true     # Enable RL training mode

Exchange Configuration (`config/exchanges.yaml`)

binance:
  enabled: true
  use_fix_api: true          # Use FIX API if available
  testnet: false            # Set to true for testing
  fee_structure:
    maker_fee: 0.000075      # 0.75 bps maker fee
    taker_fee: 0.0013        # 13 bps taker fee

coinbase:
  enabled: true
  sandbox: false            # Set to true for testing
  fee_structure:
    maker_fee: -0.000007     # -0.7 bps (rebate for institutional)
    taker_fee: 0.000075      # 7.5 bps taker fee

okx:
  enabled: true
  demo: false              # Set to true for testing
  fee_structure:
    maker_fee: -0.00001      # -1 bps (rebate for institutional)
    taker_fee: 0.00015       # 15 bps taker fee

🎯 Understanding the Strategy

Mathematical Foundation

The bot implements the Avellaneda-Stoikov model enhanced with reinforcement learning:

Reservation Price Formula:

r(t) = pm(t) - I(t) × γ × σ² × (T-t)

Optimal Spread Formula:

δ = ½[γσ²(T-t) + (1/γk)ln(1+kγ)]

Where:

pm(t): Micro-price (queue-weighted mid-price)
I(t): Current inventory position
γ: Risk aversion parameter (configurable)
σ: Volatility forecast from HAR/TimeMixer models
T-t: Time remaining in trading session

Key Strategy Components

Micro-Price Calculation: Uses order book depth to calculate fair value
Inventory Management: Automatically skews quotes to reduce inventory risk
Volatility Forecasting: HAR and TimeMixer models predict future volatility
RL Enhancement: PPO agent learns optimal parameter adjustments
Risk Controls: Multiple layers of risk management and circuit breakers

📈 Performance Monitoring

Real-time Monitoring

# View live performance dashboard
python -m monitoring.dashboard

# Check system health
python -m monitoring.health_check

# Generate performance report
python -m monitoring.performance_report --period 24h

Key Metrics to Watch

Sharpe Ratio: Risk-adjusted returns (target: >1.5)
Fill Rate: Percentage of orders filled (target: >80%)
Inventory Turnover: How quickly positions are managed
Spread Capture: Percentage of bid-ask spread captured
Adverse Selection: Losses from informed traders

🛡️ Risk Management Features

Automatic Risk Controls

Position Limits: Maximum inventory per symbol
Drawdown Protection: Automatic trading halt on losses
Volatility Circuit Breakers: Stop trading during market stress
Order Age Limits: Cancel stale quotes automatically
Market Manipulation Detection: Identify suspicious patterns

Manual Risk Controls

# Emergency stop all trading
python -c "
from strategy.main import TradingStrategy
strategy = TradingStrategy(config)
strategy.emergency_stop()
"

# Check current positions
python -c "
from risk.risk_manager import RiskManager
risk_manager = RiskManager(config)
print(risk_manager.get_portfolio_summary())
"

🔧 Advanced Usage

Custom Strategy Development

from strategy.avellaneda_stoikov import AvellanedaStoikovEngine

class CustomStrategy(AvellanedaStoikovEngine):
    def calculate_optimal_quotes(self, snapshot, volatility_forecast=None):
        # Add your custom logic here
        quotes = super().calculate_optimal_quotes(snapshot, volatility_forecast)
        
        # Example: Adjust spreads based on time of day
        import datetime
        hour = datetime.datetime.now().hour
        if 9 <= hour <= 16:  # Market hours
            quotes.spread *= 0.8  # Tighter spreads
        
        return quotes

Backtesting Historical Data

# Download historical data
python -m data.download --symbol BTC-USD --start 2024-01-01 --end 2024-12-31

# Run backtest
python -m backtesting.runner \
    --data data/historical/BTC-USD.csv \
    --config config/strategy.yaml \
    --output results/backtest_results.json

Model Training

# Train RL model on historical data
python -m models.train \
    --data-path data/historical/ \
    --model-output models/trained/ \
    --epochs 1000 \
    --symbol BTC-USD

🧪 Testing & Validation

Run Test Suite

# All tests (should show 142 passed, 1 skipped)
make test

# Specific test categories
make test-unit          # Unit tests only
make test-integration   # Integration tests only
make test-connectors    # Exchange connector tests
make test-strategy      # Strategy logic tests

Paper Trading Validation

# Run paper trading for 24 hours
python main.py --mode paper --duration 86400 --log-level DEBUG

# Analyze paper trading results
python -m analysis.paper_trading_report --log-file logs/trading.log

🚀 Production Deployment

Pre-deployment Checklist

All tests passing (make test)
Exchange API credentials configured and tested
Risk limits properly set for your account size
Monitoring and alerting systems configured
Backup and recovery procedures in place
Network latency optimized (co-location recommended)

Docker Deployment

# Build container
docker build -t hft-mm-bot .

# Run with environment file
docker run --env-file .env --name hft-bot hft-mm-bot

# View logs
docker logs -f hft-bot

Systemd Service (Linux)

# Create service file
sudo tee /etc/systemd/system/hft-mm-bot.service > /dev/null <<EOF
[Unit]
Description=HFT Market Making Bot
After=network.target

[Service]
Type=simple
User=trading
WorkingDirectory=/opt/hft_mm_bot
ExecStart=/opt/hft_mm_bot/venv/bin/python main.py --mode live
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target
EOF

# Enable and start service
sudo systemctl enable hft-mm-bot
sudo systemctl start hft-mm-bot

📊 Performance Optimization

Latency Optimization

# config/performance.yaml
performance:
  cpu_affinity: [0, 1]      # Pin to specific CPU cores
  memory_lock: true         # Lock memory to prevent swapping
  network_buffer_size: 65536 # Increase network buffer
  gc_disable: true          # Disable garbage collection during trading

Network Configuration

# Optimize network settings (Linux)
echo 'net.core.rmem_max = 134217728' >> /etc/sysctl.conf
echo 'net.core.wmem_max = 134217728' >> /etc/sysctl.conf
echo 'net.ipv4.tcp_rmem = 4096 87380 134217728' >> /etc/sysctl.conf
sysctl -p

🔍 Troubleshooting

Common Issues

1. Connection Errors

# Check network connectivity
ping api.binance.com
ping api.exchange.coinbase.com
ping www.okx.com

# Verify API credentials
python -c "
from connectors.binance import BinanceConnector
connector = BinanceConnector(config)
print(connector.test_connection())
"

2. High Latency

# Measure latency to exchanges
python -m monitoring.latency_test

# Expected results:
# Binance: <50ms
# Coinbase: <100ms
# OKX: <100ms

3. Risk Violations

# Check current risk status
python -c "
from risk.risk_manager import RiskManager
rm = RiskManager(config)
print(rm.get_risk_summary())
"

4. Model Training Issues

# Check GPU availability
python -c "import torch; print(torch.cuda.is_available())"

# Train with CPU if GPU unavailable
python -m models.train --device cpu

Debug Mode

# Run with maximum logging
python main.py --mode paper --log-level DEBUG --debug

# Enable profiling
python -m cProfile -o profile.stats main.py --mode paper

📞 Support & Resources

Documentation

API Reference: See docs/api/ directory
Strategy Guide: See docs/strategy/ directory
Risk Management: See docs/risk/ directory

Getting Help

Issues: GitHub Issues
Discussions: GitHub Discussions
Email: xusiyi2005@gmail.com

Community

Join our Discord server for real-time support
Follow development updates on Twitter
Contribute to the project on GitHub

⚠️ Important Disclaimers

Risk Warning

High Risk: Cryptocurrency trading involves substantial risk of loss
No Guarantees: Past performance does not guarantee future results
Capital Risk: Never trade with money you cannot afford to lose
Market Risk: Crypto markets are highly volatile and unpredictable

Legal Compliance

Ensure compliance with local regulations
Some jurisdictions restrict algorithmic trading
Consult with legal and financial advisors
Understand tax implications in your jurisdiction

Technical Risks

Software Bugs: Thoroughly test before live trading
Network Issues: Ensure reliable internet connection
Exchange Risks: Exchanges may experience downtime or issues
Model Risk: AI models may perform poorly in new market conditions

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Research paper: "A Comprehensive Research Plan for Profitable Cryptocurrency Market Making"
Avellaneda & Stoikov original market making framework
PPO algorithm implementation based on OpenAI research
Exchange API documentation and developer communities

🎯 Ready to Start Trading?

Test First: Always start with paper trading
Start Small: Use minimal position sizes initially
Monitor Closely: Watch performance and risk metrics
Scale Gradually: Increase size only after proven performance
Stay Informed: Keep up with market conditions and updates

Built with ❤️ for institutional-grade cryptocurrency market making

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
config		config
connectors		connectors
data		data
models		models
risk		risk
strategy		strategy
tests		tests
.env.example		.env.example
Makefile		Makefile
PR_DESCRIPTION.md		PR_DESCRIPTION.md
README.md		README.md
main.py		main.py
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt

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High-Frequency Market Making Bot with RL-Augmented Avellaneda-Stoikov Strategy

🚀 Key Features

Advanced Trading Strategy

Multi-Exchange Support

AI-Powered Forecasting

High-Performance Architecture

📋 Prerequisites

System Requirements

Exchange Account Requirements

🛠️ Installation & Setup

Step 1: Clone Repository

Step 2: Create Virtual Environment

Step 3: Install Dependencies

Step 4: Configure Environment

Step 5: Verify Installation

🚦 Quick Start Guide

1. Paper Trading Mode (Recommended First)

2. Live Trading (Production)

3. Monitor Performance

📊 Configuration Guide

Strategy Parameters (config/strategy.yaml)

Exchange Configuration (config/exchanges.yaml)

🎯 Understanding the Strategy

Mathematical Foundation

Key Strategy Components

📈 Performance Monitoring

Real-time Monitoring

Key Metrics to Watch

🛡️ Risk Management Features

Automatic Risk Controls

Manual Risk Controls

🔧 Advanced Usage

Custom Strategy Development

Backtesting Historical Data

Model Training

🧪 Testing & Validation

Run Test Suite

Paper Trading Validation

🚀 Production Deployment

Pre-deployment Checklist

Docker Deployment

Systemd Service (Linux)

📊 Performance Optimization

Latency Optimization

Network Configuration

🔍 Troubleshooting

Common Issues

Debug Mode

📞 Support & Resources

Documentation

Getting Help

Community

⚠️ Important Disclaimers

Risk Warning

Legal Compliance

Technical Risks

📄 License

🙏 Acknowledgments

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Strategy Parameters (`config/strategy.yaml`)

Exchange Configuration (`config/exchanges.yaml`)

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